• Journal of the Korea Society of Computer and Information
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• v.23 no.3
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• pp.17-23
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• 2018

In this paper, we propose the automatic detection method which find the middle part of tooth crown to start individual tooth segmentation. There have been many studies on the automation of individual tooth segmentation, but there are still many problems for full automation. Detection of middle part of tooth crown used as initial information for individual tooth segmentation is closely related to performance, but most studies are based on the assumption that they are already known or they can be represented by using a straight line. In this study, we have found that the jawbone curve is similar to the tooth alignment curve by spatially analyzing the CT image, and propose a method to automatically detect the middle part of tooth crown. The proposed method successfully uses the jawbone curves to successfully create a tooth alignment curve that is difficult to detect. As the middle part of tooth crown is in the form of a tooth alignment curve, the proposed method detects the middle part of tooth crown successfully. It has also been verified by experiments that the proposed method works well on real dental CT images.

• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.557-563
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• 2022

The rating curve is required to convert measured stage into a discharge and is developed using the measurement. In the development of the rating curve, the segmentation position is determined by considering the hydraulic characteristic and channel shape, and subjective judgment of the Hydrographer may intervene in this process. The segmentation position is so important that it determines the overall form of the rating curve, and the incorrect segmentation can cause errors in the rating curve, especially in extrapolation. In order to develop an accurate rating curve with a small number of measurements, the sections must be divided by considering hydraulic characteristic such as the cross-sectional shape. In this study, hydraulic examination methods such as stage-mean velocity, stage-area, stage-${\sqrt{Q}}$ investigated and supplemented to eliminate subjectivity in segmental positioning. Appropriateness for the segmentation position was verify in consideration of the physical meaning of the rating curve index (c).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.303-311
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• 2003

Since the MPEG-4 visual standard enables content-based functionalities, it is necessary to extract video object from natural video sequences. Segmentation algorithms can largely be classified into automatic segmentation and user-assisted segmentation. In this paper, we propose a user-assisted VOP generation method based on the geometric active contour. Since the geometric active contour, unlike the parametric active contour, employs the level set method to evolve the curve, we can draw the initial curve independent of the shape of the object. In order to generate the edge function from a smoothed image, we propose a vector-valued diffusion process in the LUV color space. We also present a discrete 3-D diffusion model for easy implementation. By combining the curve shrinkage in the vector field space with the curve expansion in the empty vector space, we can make accurate extraction of visual objects from video sequences.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.41-50
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• 2004

Since the MPEG-4 visual standard enables content-based functionalities, it is necessary to extract video objects from video sequences. Segmentation algorithms can largely be classified into two different categories: automatic segmentation and user-assisted segmentation. In this paper, we propose a new user-assisted image segmentation method based on the active contour. If we define a shape space as a set of all possible variations from the initial curve and we assume that the shape space is linear, it can be decomposed into the column space and the left null space of the shape matrix. In the proposed method, the shape space vector in the column space describes changes from the initial curve to the imaginary feature curve, and a dynamic graph search algorithm describes the detailed shape of the object in the left null space. Since we employ the shape matrix and the SUSAN operator to outline object boundaries, the proposed algorithm can ignore unwanted feature points generated by low-level image processing operations and is, therefore, applicable to images of complex background. We can also compensate for limitations of the shape matrix with a dynamic graph search algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.10
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• pp.604-614
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• 2005

Color segmentation takes great attentions since a color is an effective and robust visual cue for characterizing one object from other objects. Color segmentation is, however, suffered from color variation induced from irregular illumination changes. This paper proposes a reliable color modeling approach in HSI (Hue-Saturation-Intensity) rotor space considering intensity information by adopting B-spline curve fitting to make a mathematical model for statistical characteristics of a color with respect to brightness. It is based on the fact that color distribution of a single-colored object is not invariant with respect to brightness variations even in HS (Hue-Saturation) plane. The proposed approach is applied for the segmentation of human skin areas successfully under various illumination conditions.

• Journal of the Korea Society of Computer and Information
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• v.21 no.12
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• pp.125-130
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• 2016

Automatic quality inspection by computer vision can be applied and give a solution to the pharmaceutical industry field. Pharmaceutical capsule can be easily affected by flaws like dents, cracks, holes, etc. In order to solve the quality inspection problem, it is required computationally efficient image processing technique like thresholding, boundary edge detection and segmentation and some automated systems are available but they are very expensive to use. In this paper, we have developed a dented capsule image processing technique using edge-based image segmentation, TLS(Total Least Squares) curve fitting technique and adopted low cost camera module for capsule image capturing. We have tested and evaluated the accuracy, training and testing time of the classification recognition algorithms like PCA(Principal Component Analysis), ICA(Independent Component Analysis) and SVM(Support Vector Machine) to show the performance. With the result, PCA, ICA has low accuracy, but SVM has good accuracy to use for classifying the dented capsule.

• The Journal of Korean Association of Computer Education
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• v.10 no.6
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• pp.91-105
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• 2007

The Hough transform is a popular method for ellipse detection from image. But the method wastes time and memory space severely. And the existing methods for elliptical arc segmentation are very sensitive to noise or detect improper breakpoints. In this paper a fast method is proposed for the segmentation and detection of elliptical arcs from digital curve using its area. Experimental results show that the proposed method segments and detects elliptical arcs from noisy curves and the average of the distance errors between the fitted arc and given curve is within a threshold.

• Journal of Internet Computing and Services
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• v.9 no.1
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• pp.145-158
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• 2008

The detected arcs by the cone intersection(CI) method exist within a tolerant error from the original curve. But the position of breakpoint by the CI method is sensitive to the tolerant error. The deviation of breakpoint position makes ill effects to the detection of arc center or radius. In this paper a improved CI method for center detection and arc segmentation is proposed. The detected arcs from curves by the proposed method have very stable mean of deviations from the original curves. The experimental results show that the position of breakpoint by the proposed method is closer to real position than the one by other methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.967-975
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• 2010

Image segmentation is one of the first steps leading to image analysis and interpretation, which is to distinguish objects from background. However, the active contour model can't exactly extract the desired objects because the phase only is 2. In this paper, we propose the method which can find the desired contours by composing the initial curve near the objects which have intensities of special range. The initial curve is calculated by the histogram equalization, the Gaussian equalization, and the threshold. The proposed method reduce the calculation speed and exactly detect the wanted objects because the initial curve set near by interested area. The proposed method also shows more efficient than the active contour model in the results applied the CT and MR images.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.73-76
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• 2001

A segmentation is a prior processing for medical image analysis and 3D reconstruction. This Paper provides the method to segment 3D Visible Human color images. Firstly, the reference images that have a initial curve are segmented using Balloon and the results are propagated to the adjacent images. In the propagation processing, the result of the adjacent slice is modified by Edge-limited SRG Finally, the 3D Balloon improves the segmentation results of each 2D slice. the proposed method's performance was verified through the experiments to segment thigh muscles of Visible Human color images.